论文标题
双重对比网络有效的抽象推理
Effective Abstract Reasoning with Dual-Contrast Network
论文作者
论文摘要
为了提高机器的抽象推理能力，我们旨在通过神经网络解决Raven的渐进式矩阵（RPM），因为解决RPM难题与人类智能高度相关。与以前使用辅助注释或假设隐藏规则产生适当特征表示的方法不同，我们仅将每个问题的基础真实答案用于模型学习，旨在使智能代理具有少量监督的强大学习能力。基于RPM问题公式，在第三行/列的缺失条目中填充的正确答案必须最好地满足前两个行/列之间共享的相同规则。因此，我们设计了一个简单而有效的双对照网络（DCNET）来利用RPM难题的固有结构。具体而言，规则对比模块旨在比较填充的行/列与前两个行/列之间的潜在规则。选择对比模块旨在增加候选选择之间的相对差异。 RAVEN和PGM数据集的实验结果表明，DCNET的表现优于最先进的方法，较大的边距为5.77％。几乎没有训练样本和模型概括的进一步实验也显示了DCNET的有效性。代码可在https://github.com/visiontao/dcnet上找到。
As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the ground truth answer of each question for model learning, aiming for an intelligent agent to have a strong learning capability with a small amount of supervision. Based on the RPM problem formulation, the correct answer filled into the missing entry of the third row/column has to best satisfy the same rules shared between the first two rows/columns. Thus we design a simple yet effective Dual-Contrast Network (DCNet) to exploit the inherent structure of RPM puzzles. Specifically, a rule contrast module is designed to compare the latent rules between the filled row/column and the first two rows/columns; a choice contrast module is designed to increase the relative differences between candidate choices. Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. Code is available at https://github.com/visiontao/dcnet.
